Trajectory Optimization and Machine Learning to Design Feedback Controllers for Bipedal Robots with Provable Stability

This thesis combines recent advances in trajectory optimization of hybrid dynamical systems with machine learning and geometric control theory to achieve unprecedented performance in bipedal robot locomotion. The work greatly expands the class of robot models for which feedback controllers can be designed with provable stability. The methods are widely applicable beyond bipedal robots, including exoskeletons, and prostheses, and eventually, drones, ADAS, and other highly automated machines. One main idea of this thesis is to greatly expand the use of multiple trajectories in the design of a stabilizing controller. The computation of many trajectories is now feasible due to new optimization tools. The computations are not fast enough to apply in the real-time, however, so they are not feasible for model predictive control (MPC). The offline “library” approach will encounter the curse of dimensionality for the high-dimensional models common in bipedal robots. To overcome these obstructions, we embed a stable walking motion in an attractive low-dimensional surface of the system's state space. The periodic orbit is now an attractor of the low-dimensional state-variable model but is not attractive in the full-order system. We then use the special structure of mechanical models associated with bipedal robots to embed the low-dimensional model in the original model in such a manner that the desired walking motions are locally exponentially stable. The ultimate solution in this thesis will generate model-based feedback controllers for bipedal robots, in such a way that the closed-loop system has a large stability basin, exhibits highly agile, dynamic behavior, and can deal with significant perturbations coming from the environment. In the case of bipeds: “model-based” means that the controller will be designed on the basis of the full floating-base dynamic model of the robot, and not a simplified model, such as the LIP (Linear Inverted Pendulum). By “agile and dynamic” is meant that the robot moves at the speed of a normal human or faster while walking off a curb. By “significant perturbation” is meant a human tripping, and while falling, throwing his/her full weight into the back of the robot.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145992/1/xda_1.pd

Towards dynamic Narrow path walking on NU's Husky

This research focuses on enabling Northeastern University's Husky, a multi-modal quadrupedal robot, to navigate narrow paths akin to various animals in nature. The Husky is equipped with thrusters to stabilize its body during dynamic maneuvers, addressing challenges inherent in aerial-legged systems. The approach involves modeling the robot as HROM (Husky Reduced Model) and creating an optimal control framework using linearized dynamics for narrow path walking. The thesis introduces a gait scheduling method to generate an open-loop walking gait and validates these gaits through a high-fidelity Simscape simulation. Experimental results of the open-loop walking are presented, accompanied by potential directions for advancing this robotic system.Comment: 60 pages, 27 figure

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Optimal control of an underactuated bipedal robot

Bipedal robots represent a unique class of control problems that combine many of the most difficult elements of nonlinear control. These robots are typically designed to be mobile and as such have limited energy and actuator authority making efficiency a prime concern. Unlike wheeled robots, legged robots must transition between different equations of motion as legs make and break contact with the ground, aggravating the complexities of hybrid dynamics. Furthermore, small feet and series elastic actuation of joints leads to an underactuated system, causing traditional nonlinear control methods to struggle. This thesis describes and validates a general framework for efficiently controlling systems with the properties most problematic in bipedal robots, i.e., nonlinearity, hybrid dynamics, and underactuation. This process is two-step in that, first, an optimal trajectory is generated using direct collocation trajectory optimization, and second, the feasible trajectory is stabilized using a time varying linear quadratic regulator. We demonstrate this process on a number of toy problems including the triple pendulum on a cart and a reduced order template for a running robot. This approach is then applied to the bipedal robot ATRIAS in simulation in order to achieve efficient, dynamic, and robust locomotion behaviors

Hierarchical neural control of human postural balance and bipedal walking in sagittal plane

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 177-192).The cerebrocerebellar system has been known to be a central part in human motion control and execution. However, engineering descriptions of the system, especially in relation to lower body motion, have been very limited. This thesis proposes an integrated hierarchical neural model of sagittal planar human postural balance and biped walking to 1) investigate an explicit mechanism of the cerebrocerebellar and other related neural systems, 2) explain the principles of human postural balancing and biped walking control in terms of the central nervous systems, and 3) provide a biologically inspired framework for the design of humanoid or other biomorphic robot locomotion. The modeling was designed to confirm neurophysiological plausibility and achieve practical simplicity as well. The combination of scheduled long-loop proprioceptive and force feedback represents the cerebrocerebellar system to implement postural balance strategies despite the presence of signal transmission delays and phase lags. The model demonstrates that the postural control can be substantially linear within regions of the kinematic state-space with switching driven by sensed variables.(cont.) A improved and simplified version of the cerebrocerebellar system is combined with the spinal pattern generation to account for human nominal walking and various robustness tasks. The synergy organization of the spinal pattern generation simplifies control of joint actuation. The substantial decoupling of the various neural circuits facilitates generation of modulated behaviors. This thesis suggests that kinematic control with no explicit internal model of body dynamics may be sufficient for those lower body motion tasks and play a common role in postural balance and walking. All simulated performances are evaluated with respect to actual observations of kinematics, electromyogram, etc.by Sungho JoPh.D

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Streamlined sim-to-real transfer for deep-reinforcement learning in robotics locomotion

Legged robots possess superior mobility compared to other machines, yet designing controllers for them can be challenging. Classic control methods require engineers to distill their knowledge into controllers, which is time-consuming and limiting when approaching dynamic tasks in unknown environments. Conversely, learning- based methods that gather knowledge from data can potentially unlock the versatility of legged systems. In this thesis, we propose a novel approach called CPG-Actor, which incor- porates feedback into a fully differentiable Central Pattern Generator (CPG) formulation using neural networks and Deep-Reinforcement Learning (RL). This approach achieves approximately twenty times better training performance compared to previous methods and provides insights into the impact of training on the distribution of parameters in both the CPGs and MLP feedback network. Adopting Deep-RL to design controllers comes at the expense of gathering extensive data, typically done in simulation to reduce time. However, controllers trained with data collected in simulation often lose performance when deployed in the real world, referred to as the sim-to-real gap. To address this, we propose a new method called Extended Random Force Injection (ERFI), which randomizes only two parameters to allow for sim-to-real transfer of locomotion controllers. ERFI demonstrated high robustness when varying masses of the base, or attaching a manipulator arm to the robot during testing, and achieved competitive performance comparable to standard randomization techniques. Furthermore, we propose a new method called Roll-Drop to enhance the robustness of Deep-RL policies to observation noise. Roll-Drop introduces dropout during rollout, achieving an 80% success rate when tested with up to 25% noise injected in the observations. Finally, we adopted model-free controllers to enable omni-directional bipedal lo- comotion on point feet with a quadruped robot without any hardware modification or external support. Despite the limitations posed by the quadruped’s hardware, the study considers this a perfect benchmark task to assess the shortcomings of sim- to-real techniques and unlock future avenues for the legged robotics community. Overall, this thesis demonstrates the potential of learning-based methods to design dynamic and robust controllers for legged robots while limiting the effort needed for sim-to-real transfer